Better random forests

classifiers/__init__.py

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+from classifiers.evaluation import *

classifiers/evaluation.py

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+from data.data_processing import process_train_test
+from sklearn.model_selection import cross_validate, ParameterGrid
+
+
+def crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=5):
+    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    classifier_params = {key: params[key] for key in params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    X_train, _ = process_train_test(process_params, experiments_train, metadata_train, scale=True)
+    clf = classifier(**classifier_params)
+    return cross_validate(clf, X_train, y_train.to_numpy().ravel(), cv=cv, return_estimator=True)
+
+
+def param_grid_search(classifier, param_grid, experiments_train, metadata_train, y_train, cv=5):
+    results = []
+    for params in ParameterGrid(param_grid):
+        try:
+            results.append([params, crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=cv)])
+            print(results[-1])
+        except Exception as e:
+            pass  # print(params, e)
+    return results
+
+
+def evaluate_classifier_params(classifier, params, X_train, y_train, X_test, y_test, iters=10):
+    train_score_mean = 0
+    test_score_mean = 0
+    for i in range(iters):
+        clf = classifier(**params)
+        clf.fit(X_train, y_train.to_numpy().ravel())
+        train_score_mean += clf.score(X_train, y_train.to_numpy().ravel())
+        test_score_mean += clf.score(X_test, y_test.to_numpy().ravel())
+    return train_score_mean / iters, test_score_mean / iters
+
+
+